PCA
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Principal Component Analysis: a dimensionality reduction technique that finds orthogonal directions of maximum variance in data. Computed by finding the eigenvectors of the covariance matrix. The top k eigenvectors (by eigenvalue magnitude) capture the most important patterns. Widely used in data visualization, feature extraction, and noise reduction.